分类器(UML)
计算机科学
插补(统计学)
生成语法
对抗制
缺少数据
生成对抗网络
机器学习
人工智能
纵向数据
数据挖掘
模式识别(心理学)
深度学习
作者
Sharon Torao Pingi,Richi Nayak,Abul Bashar
出处
期刊:ACM Transactions on Knowledge Discovery From Data
[Association for Computing Machinery]
日期:2024-03-26
卷期号:18 (5): 1-25
摘要
Early classification of longitudinal data remains an active area of research today. The complexity of these datasets and the high rates of missing data caused by irregular sampling present data-level challenges for the Early Longitudinal Data Classification (ELDC) problem. Coupled with the algorithmic challenge of optimising the opposing objectives of early classification (i.e., earliness and accuracy), ELDC becomes a non-trivial task. Inspired by the generative power and utility of the Generative Adversarial Network (GAN), we propose a novel context-conditional, longitudinal early classifier GAN (LEC-GAN). This model utilises informative missingness, static features and earlier observations to improve the ELDC objective. It achieves this by incorporating ELDC as an auxiliary task within an imputation optimization process. Our experiments on several datasets demonstrate that LEC-GAN outperforms all relevant baselines in terms of F1 scores while increasing the earliness of prediction.
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